Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations10094
Missing cells63133
Missing cells (%)20.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.6 MiB
Average record size in memory2.4 KiB

Variable types

Numeric11
Text5
Categorical6
Boolean7
Unsupported1

Alerts

garage has constant value "False" Constant
Etat is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
NewDelegation is highly overall correlated with NewState and 2 other fieldsHigh correlation
NewDelegationReport is highly overall correlated with newMunipReportHigh correlation
NewState is highly overall correlated with NewDelegation and 2 other fieldsHigh correlation
NewStateReport is highly overall correlated with delegation and 1 other fieldsHigh correlation
age is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
air_conditioning is highly overall correlated with heatingHigh correlation
bathrooms is highly overall correlated with Etat and 2 other fieldsHigh correlation
bedrooms is highly overall correlated with Etat and 3 other fieldsHigh correlation
delegation is highly overall correlated with NewDelegation and 7 other fieldsHigh correlation
equipped_kitchen is highly overall correlated with delegationHigh correlation
floor is highly overall correlated with rez_de_chaussee_countHigh correlation
heating is highly overall correlated with air_conditioningHigh correlation
newMunipReport is highly overall correlated with NewDelegationReportHigh correlation
price is highly overall correlated with delegation and 2 other fieldsHigh correlation
rez_de_chaussee_count is highly overall correlated with floorHigh correlation
rooms is highly overall correlated with bedroomsHigh correlation
state is highly overall correlated with Etat and 5 other fieldsHigh correlation
surface is highly overall correlated with Etat and 4 other fieldsHigh correlation
location has 3793 (37.6%) missing values Missing
state has 2057 (20.4%) missing values Missing
rooms has 5292 (52.4%) missing values Missing
etage_count has 2007 (19.9%) missing values Missing
rez_de_chaussee_count has 2007 (19.9%) missing values Missing
floor has 4217 (41.8%) missing values Missing
date has 6908 (68.4%) missing values Missing
floor_description has 5073 (50.3%) missing values Missing
Etat has 8881 (88.0%) missing values Missing
age has 8833 (87.5%) missing values Missing
delegation has 7141 (70.7%) missing values Missing
municipality has 6898 (68.3%) missing values Missing
price is highly skewed (γ1 = 35.76610954) Skewed
bedrooms is highly skewed (γ1 = 80.68649431) Skewed
bathrooms is highly skewed (γ1 = 78.41789243) Skewed
surface is highly skewed (γ1 = 58.18694302) Skewed
date is an unsupported type, check if it needs cleaning or further analysis Unsupported
etage_count has 3838 (38.0%) zeros Zeros
rez_de_chaussee_count has 6688 (66.3%) zeros Zeros
floor has 1164 (11.5%) zeros Zeros

Reproduction

Analysis started2025-02-07 20:55:56.876677
Analysis finished2025-02-07 20:56:15.444589
Duration18.57 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

price
Real number (ℝ)

High correlation  Skewed 

Distinct877
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean765530.4
Minimum10666
Maximum5.85 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-02-07T21:56:15.523587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10666
5-th percentile110000
Q1185000
median280000
Q3420000
95-th percentile808146.85
Maximum5.85 × 108
Range5.8498933 × 108
Interquartile range (IQR)235000

Descriptive statistics

Standard deviation12483635
Coefficient of variation (CV)16.307171
Kurtosis1408.3731
Mean765530.4
Median Absolute Deviation (MAD)110000
Skewness35.76611
Sum7.7272639 × 109
Variance1.5584115 × 1014
MonotonicityNot monotonic
2025-02-07T21:56:15.646591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250000 270
 
2.7%
220000 204
 
2.0%
180000 194
 
1.9%
230000 193
 
1.9%
350000 192
 
1.9%
240000 177
 
1.8%
150000 169
 
1.7%
320000 167
 
1.7%
200000 165
 
1.6%
190000 163
 
1.6%
Other values (867) 8200
81.2%
ValueCountFrequency (%)
10666 1
< 0.1%
11111 1
< 0.1%
12000 2
< 0.1%
15000 2
< 0.1%
18000 1
< 0.1%
19000 2
< 0.1%
20000 1
< 0.1%
22222 2
< 0.1%
23000 1
< 0.1%
24700 1
< 0.1%
ValueCountFrequency (%)
585000000 1
< 0.1%
570000000 1
< 0.1%
520000000 1
< 0.1%
330000000 2
< 0.1%
290000000 1
< 0.1%
280000000 2
< 0.1%
235000000 2
< 0.1%
230000000 1
< 0.1%
98218890 1
< 0.1%
80000000 1
< 0.1%

location
Text

Missing 

Distinct773
Distinct (%)12.3%
Missing3793
Missing (%)37.6%
Memory size625.1 KiB
2025-02-07T21:56:15.972634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length83
Median length71
Mean length14.762895
Min length0

Characters and Unicode

Total characters93021
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique419 ?
Unique (%)6.6%

Sample

1st rowJardins de Carthage
2nd rowLes Jardins du lac 2
3rd rowLes berges du lac 1
4th rowEnnasr 1-2
5th rowAouina
ValueCountFrequency (%)
tunis 1485
 
8.8%
el 1457
 
8.6%
la 1114
 
6.6%
cité 740
 
4.4%
soukra 652
 
3.8%
582
 
3.4%
aouina 492
 
2.9%
ariana 483
 
2.8%
ennasr 398
 
2.3%
carthage 339
 
2.0%
Other values (573) 9227
54.4%
2025-02-07T21:56:16.394544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 12088
 
13.0%
11486
 
12.3%
n 6368
 
6.8%
i 5686
 
6.1%
u 5201
 
5.6%
e 5061
 
5.4%
r 5041
 
5.4%
o 3931
 
4.2%
s 3890
 
4.2%
l 3390
 
3.6%
Other values (87) 30879
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12088
 
13.0%
11486
 
12.3%
n 6368
 
6.8%
i 5686
 
6.1%
u 5201
 
5.6%
e 5061
 
5.4%
r 5041
 
5.4%
o 3931
 
4.2%
s 3890
 
4.2%
l 3390
 
3.6%
Other values (87) 30879
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12088
 
13.0%
11486
 
12.3%
n 6368
 
6.8%
i 5686
 
6.1%
u 5201
 
5.6%
e 5061
 
5.4%
r 5041
 
5.4%
o 3931
 
4.2%
s 3890
 
4.2%
l 3390
 
3.6%
Other values (87) 30879
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12088
 
13.0%
11486
 
12.3%
n 6368
 
6.8%
i 5686
 
6.1%
u 5201
 
5.6%
e 5061
 
5.4%
r 5041
 
5.4%
o 3931
 
4.2%
s 3890
 
4.2%
l 3390
 
3.6%
Other values (87) 30879
33.2%

state
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing2057
Missing (%)20.4%
Memory size608.8 KiB
Ariana
3406 
Tunis
2880 
Ben arous
1457 
Manouba
 
294

Length

Max length9
Median length7
Mean length6.2220978
Min length5

Characters and Unicode

Total characters50007
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTunis
2nd rowTunis
3rd rowTunis
4th rowAriana
5th rowAriana

Common Values

ValueCountFrequency (%)
Ariana 3406
33.7%
Tunis 2880
28.5%
Ben arous 1457
14.4%
Manouba 294
 
2.9%
(Missing) 2057
20.4%

Length

2025-02-07T21:56:16.531405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T21:56:16.672505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ariana 3406
35.9%
tunis 2880
30.3%
ben 1457
15.3%
arous 1457
15.3%
manouba 294
 
3.1%

Most occurring characters

ValueCountFrequency (%)
a 8857
17.7%
n 8037
16.1%
i 6286
12.6%
r 4863
9.7%
u 4631
9.3%
s 4337
8.7%
A 3406
 
6.8%
T 2880
 
5.8%
o 1751
 
3.5%
B 1457
 
2.9%
Other values (4) 3502
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8857
17.7%
n 8037
16.1%
i 6286
12.6%
r 4863
9.7%
u 4631
9.3%
s 4337
8.7%
A 3406
 
6.8%
T 2880
 
5.8%
o 1751
 
3.5%
B 1457
 
2.9%
Other values (4) 3502
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8857
17.7%
n 8037
16.1%
i 6286
12.6%
r 4863
9.7%
u 4631
9.3%
s 4337
8.7%
A 3406
 
6.8%
T 2880
 
5.8%
o 1751
 
3.5%
B 1457
 
2.9%
Other values (4) 3502
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8857
17.7%
n 8037
16.1%
i 6286
12.6%
r 4863
9.7%
u 4631
9.3%
s 4337
8.7%
A 3406
 
6.8%
T 2880
 
5.8%
o 1751
 
3.5%
B 1457
 
2.9%
Other values (4) 3502
 
7.0%

bedrooms
Real number (ℝ)

High correlation  Skewed 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5974837
Minimum0
Maximum875
Zeros46
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-02-07T21:56:16.771515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum875
Range875
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.477479
Coefficient of variation (CV)3.6487155
Kurtosis7191.0616
Mean2.5974837
Median Absolute Deviation (MAD)1
Skewness80.686494
Sum26219
Variance89.822607
MonotonicityNot monotonic
2025-02-07T21:56:16.861506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 4079
40.4%
3 3497
34.6%
1 1347
 
13.3%
4 928
 
9.2%
5 177
 
1.8%
0 46
 
0.5%
6 13
 
0.1%
216 3
 
< 0.1%
10 1
 
< 0.1%
7 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 46
 
0.5%
1 1347
 
13.3%
2 4079
40.4%
3 3497
34.6%
4 928
 
9.2%
5 177
 
1.8%
6 13
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
875 1
 
< 0.1%
216 3
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 13
 
0.1%
5 177
 
1.8%
4 928
 
9.2%
3 3497
34.6%
2 4079
40.4%

rooms
Real number (ℝ)

High correlation  Missing 

Distinct21
Distinct (%)0.4%
Missing5292
Missing (%)52.4%
Infinite0
Infinite (%)0.0%
Mean3.0547688
Minimum0
Maximum34
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-02-07T21:56:16.957509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum34
Range34
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6102538
Coefficient of variation (CV)0.52712787
Kurtosis129.83003
Mean3.0547688
Median Absolute Deviation (MAD)1
Skewness8.125988
Sum14669
Variance2.5929173
MonotonicityNot monotonic
2025-02-07T21:56:17.058504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 1780
 
17.6%
4 1164
 
11.5%
2 1082
 
10.7%
1 429
 
4.3%
5 266
 
2.6%
6 40
 
0.4%
7 16
 
0.2%
10 4
 
< 0.1%
0 3
 
< 0.1%
24 2
 
< 0.1%
Other values (11) 16
 
0.2%
(Missing) 5292
52.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 429
 
4.3%
2 1082
10.7%
3 1780
17.6%
4 1164
11.5%
5 266
 
2.6%
6 40
 
0.4%
7 16
 
0.2%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
34 2
< 0.1%
32 1
< 0.1%
31 1
< 0.1%
25 1
< 0.1%
24 2
< 0.1%
23 2
< 0.1%
22 1
< 0.1%
14 1
< 0.1%
13 2
< 0.1%
12 1
< 0.1%

bathrooms
Real number (ℝ)

High correlation  Skewed 

Distinct10
Distinct (%)0.1%
Missing10
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.2302658
Minimum0
Maximum130
Zeros19
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-02-07T21:56:17.157509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum130
Range130
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3956238
Coefficient of variation (CV)1.1344084
Kurtosis7195.108
Mean1.2302658
Median Absolute Deviation (MAD)0
Skewness78.417892
Sum12406
Variance1.9477658
MonotonicityNot monotonic
2025-02-07T21:56:17.256507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 8219
81.4%
2 1552
 
15.4%
3 253
 
2.5%
4 28
 
0.3%
0 19
 
0.2%
5 8
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
23 1
 
< 0.1%
130 1
 
< 0.1%
(Missing) 10
 
0.1%
ValueCountFrequency (%)
0 19
 
0.2%
1 8219
81.4%
2 1552
 
15.4%
3 253
 
2.5%
4 28
 
0.3%
5 8
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
23 1
 
< 0.1%
130 1
 
< 0.1%
ValueCountFrequency (%)
130 1
 
< 0.1%
23 1
 
< 0.1%
7 1
 
< 0.1%
6 2
 
< 0.1%
5 8
 
0.1%
4 28
 
0.3%
3 253
 
2.5%
2 1552
 
15.4%
1 8219
81.4%
0 19
 
0.2%

surface
Real number (ℝ)

High correlation  Skewed 

Distinct370
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.77144
Minimum-100
Maximum159999
Zeros5
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size79.0 KiB
2025-02-07T21:56:17.372505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile55
Q190
median117
Q3155
95-th percentile229.35
Maximum159999
Range160099
Interquartile range (IQR)65

Descriptive statistics

Standard deviation2372.581
Coefficient of variation (CV)13.498103
Kurtosis3484.256
Mean175.77144
Median Absolute Deviation (MAD)33
Skewness58.186943
Sum1774236.9
Variance5629140.8
MonotonicityNot monotonic
2025-02-07T21:56:17.494848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 511
 
5.1%
120 346
 
3.4%
90 328
 
3.2%
110 291
 
2.9%
80 256
 
2.5%
150 220
 
2.2%
190.2748644 208
 
2.1%
130 198
 
2.0%
70 189
 
1.9%
140 186
 
1.8%
Other values (360) 7361
72.9%
ValueCountFrequency (%)
-100 1
 
< 0.1%
0 5
 
< 0.1%
1 46
0.5%
5 5
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 37
0.4%
12 2
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
159999 1
 
< 0.1%
123456 2
 
< 0.1%
19129 1
 
< 0.1%
13864 2
 
< 0.1%
5914 1
 
< 0.1%
5000 1
 
< 0.1%
1900 1
 
< 0.1%
1762 1
 
< 0.1%
1234 5
< 0.1%
1158 1
 
< 0.1%

parking
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
True
5345 
False
4749 
ValueCountFrequency (%)
True 5345
53.0%
False 4749
47.0%
2025-02-07T21:56:17.663627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

equipped_kitchen
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
False
8265 
True
1829 
ValueCountFrequency (%)
False 8265
81.9%
True 1829
 
18.1%
2025-02-07T21:56:17.751626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

newMunipReport
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95743939
Minimum0.44444444
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-02-07T21:56:17.846656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.44444444
5-th percentile0.75
Q10.95238095
median1
Q31
95-th percentile1
Maximum1
Range0.55555556
Interquartile range (IQR)0.047619048

Descriptive statistics

Standard deviation0.080747167
Coefficient of variation (CV)0.084336583
Kurtosis3.0536048
Mean0.95743939
Median Absolute Deviation (MAD)0
Skewness-1.9612507
Sum9664.3932
Variance0.0065201049
MonotonicityNot monotonic
2025-02-07T21:56:18.068485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7102
70.4%
0.96 250
 
2.5%
0.8333333333 241
 
2.4%
0.8571428571 185
 
1.8%
0.8 168
 
1.7%
0.9411764706 126
 
1.2%
0.9166666667 119
 
1.2%
0.75 118
 
1.2%
0.9230769231 105
 
1.0%
0.7692307692 90
 
0.9%
Other values (76) 1590
 
15.8%
ValueCountFrequency (%)
0.4444444444 2
 
< 0.1%
0.4615384615 1
 
< 0.1%
0.5 1
 
< 0.1%
0.5333333333 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6 1
 
< 0.1%
0.625 1
 
< 0.1%
0.6315789474 3
 
< 0.1%
0.6428571429 1
 
< 0.1%
0.6666666667 55
0.5%
ValueCountFrequency (%)
1 7102
70.4%
0.9743589744 66
 
0.7%
0.972972973 45
 
0.4%
0.9714285714 8
 
0.1%
0.9696969697 2
 
< 0.1%
0.9677419355 9
 
0.1%
0.9655172414 15
 
0.1%
0.962962963 11
 
0.1%
0.96 250
 
2.5%
0.9565217391 42
 
0.4%
Distinct242
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size678.2 KiB
2025-02-07T21:56:18.354940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length29
Median length22
Mean length10.100852
Min length5

Characters and Unicode

Total characters101958
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.3%

Sample

1st rowJARDINS DE CARTHAGE
2nd rowLES JARDINS
3rd rowBERGE DU LAC
4th rowENNASR
5th rowAOUINA
ValueCountFrequency (%)
el 1635
 
8.7%
aouina 912
 
4.8%
soukra 908
 
4.8%
ennasr 732
 
3.9%
jardins 632
 
3.4%
ain 586
 
3.1%
zaghouan 586
 
3.1%
carthage 499
 
2.7%
menzah 490
 
2.6%
de 486
 
2.6%
Other values (245) 11339
60.3%
2025-02-07T21:56:18.761087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 15577
15.3%
E 9597
 
9.4%
8711
 
8.5%
R 7661
 
7.5%
N 7469
 
7.3%
O 5693
 
5.6%
I 5408
 
5.3%
U 5276
 
5.2%
L 4591
 
4.5%
S 4205
 
4.1%
Other values (30) 27770
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 15577
15.3%
E 9597
 
9.4%
8711
 
8.5%
R 7661
 
7.5%
N 7469
 
7.3%
O 5693
 
5.6%
I 5408
 
5.3%
U 5276
 
5.2%
L 4591
 
4.5%
S 4205
 
4.1%
Other values (30) 27770
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 15577
15.3%
E 9597
 
9.4%
8711
 
8.5%
R 7661
 
7.5%
N 7469
 
7.3%
O 5693
 
5.6%
I 5408
 
5.3%
U 5276
 
5.2%
L 4591
 
4.5%
S 4205
 
4.1%
Other values (30) 27770
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 15577
15.3%
E 9597
 
9.4%
8711
 
8.5%
R 7661
 
7.5%
N 7469
 
7.3%
O 5693
 
5.6%
I 5408
 
5.3%
U 5276
 
5.2%
L 4591
 
4.5%
S 4205
 
4.1%
Other values (30) 27770
27.2%

NewDelegationReport
Real number (ℝ)

High correlation 

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96295522
Minimum0.44444444
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-02-07T21:56:18.896086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.44444444
5-th percentile0.69565217
Q11
median1
Q31
95-th percentile1
Maximum1
Range0.55555556
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.097442248
Coefficient of variation (CV)0.10119084
Kurtosis5.2489578
Mean0.96295522
Median Absolute Deviation (MAD)0
Skewness-2.5706652
Sum9720.07
Variance0.0094949918
MonotonicityNot monotonic
2025-02-07T21:56:19.013108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8545
84.7%
0.6666666667 233
 
2.3%
0.75 158
 
1.6%
0.7058823529 90
 
0.9%
0.9090909091 86
 
0.9%
0.7368421053 69
 
0.7%
0.9333333333 64
 
0.6%
0.8235294118 60
 
0.6%
0.625 58
 
0.6%
0.8888888889 57
 
0.6%
Other values (61) 674
 
6.7%
ValueCountFrequency (%)
0.4444444444 3
 
< 0.1%
0.4545454545 1
 
< 0.1%
0.5 2
 
< 0.1%
0.5333333333 1
 
< 0.1%
0.5454545455 3
 
< 0.1%
0.56 1
 
< 0.1%
0.5714285714 7
0.1%
0.5833333333 5
< 0.1%
0.5882352941 9
0.1%
0.5925925926 1
 
< 0.1%
ValueCountFrequency (%)
1 8545
84.7%
0.9677419355 10
 
0.1%
0.9655172414 3
 
< 0.1%
0.96 1
 
< 0.1%
0.9565217391 4
 
< 0.1%
0.9523809524 16
 
0.2%
0.9473684211 27
 
0.3%
0.9411764706 37
 
0.4%
0.9375 1
 
< 0.1%
0.9333333333 64
 
0.6%

NewDelegation
Categorical

High correlation 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size650.6 KiB
SOUKRA
2304 
MARSA
1548 
EL MNIHLA
656 
ARIANA VILLE
569 
RADES
471 
Other values (44)
4546 

Length

Max length29
Median length19
Mean length8.0294234
Min length4

Characters and Unicode

Total characters81049
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowMARSA
2nd rowBAB BHAR
3rd rowRADES
4th rowEL MNIHLA
5th rowSOUKRA

Common Values

ValueCountFrequency (%)
SOUKRA 2304
22.8%
MARSA 1548
15.3%
EL MNIHLA 656
 
6.5%
ARIANA VILLE 569
 
5.6%
RADES 471
 
4.7%
EL MENZAH 457
 
4.5%
EL MOUROUJ 373
 
3.7%
MANOUBA 306
 
3.0%
RAOUED 305
 
3.0%
CARTHAGE 284
 
2.8%
Other values (39) 2821
27.9%

Length

2025-02-07T21:56:19.135381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
el 2388
15.9%
soukra 2304
15.4%
marsa 1548
 
10.3%
mnihla 656
 
4.4%
ariana 569
 
3.8%
ville 569
 
3.8%
rades 471
 
3.1%
menzah 457
 
3.0%
mourouj 373
 
2.5%
medina 321
 
2.1%
Other values (57) 5336
35.6%

Most occurring characters

ValueCountFrequency (%)
A 14478
17.9%
R 7653
9.4%
E 7126
 
8.8%
L 5633
 
7.0%
O 5371
 
6.6%
U 5048
 
6.2%
S 4939
 
6.1%
4899
 
6.0%
M 4402
 
5.4%
I 3547
 
4.4%
Other values (16) 17953
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81049
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 14478
17.9%
R 7653
9.4%
E 7126
 
8.8%
L 5633
 
7.0%
O 5371
 
6.6%
U 5048
 
6.2%
S 4939
 
6.1%
4899
 
6.0%
M 4402
 
5.4%
I 3547
 
4.4%
Other values (16) 17953
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81049
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 14478
17.9%
R 7653
9.4%
E 7126
 
8.8%
L 5633
 
7.0%
O 5371
 
6.6%
U 5048
 
6.2%
S 4939
 
6.1%
4899
 
6.0%
M 4402
 
5.4%
I 3547
 
4.4%
Other values (16) 17953
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81049
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 14478
17.9%
R 7653
9.4%
E 7126
 
8.8%
L 5633
 
7.0%
O 5371
 
6.6%
U 5048
 
6.2%
S 4939
 
6.1%
4899
 
6.0%
M 4402
 
5.4%
I 3547
 
4.4%
Other values (16) 17953
22.2%

NewStateReport
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99636046
Minimum0.5
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-02-07T21:56:19.231382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.037257219
Coefficient of variation (CV)0.037393314
Kurtosis105.25278
Mean0.99636046
Median Absolute Deviation (MAD)0
Skewness-10.293379
Sum10057.263
Variance0.0013881004
MonotonicityNot monotonic
2025-02-07T21:56:19.334617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 9996
99.0%
0.6153846154 30
 
0.3%
0.6666666667 19
 
0.2%
0.6 17
 
0.2%
0.5714285714 12
 
0.1%
0.625 6
 
0.1%
0.6315789474 2
 
< 0.1%
0.5 2
 
< 0.1%
0.5454545455 2
 
< 0.1%
0.9230769231 2
 
< 0.1%
Other values (6) 6
 
0.1%
ValueCountFrequency (%)
0.5 2
 
< 0.1%
0.5454545455 2
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 12
 
0.1%
0.5882352941 1
 
< 0.1%
0.6 17
0.2%
0.6153846154 30
0.3%
0.625 6
 
0.1%
0.6315789474 2
 
< 0.1%
0.6666666667 19
0.2%
ValueCountFrequency (%)
1 9996
99.0%
0.9230769231 2
 
< 0.1%
0.8 1
 
< 0.1%
0.75 1
 
< 0.1%
0.7272727273 1
 
< 0.1%
0.7058823529 1
 
< 0.1%
0.6666666667 19
 
0.2%
0.6315789474 2
 
< 0.1%
0.625 6
 
0.1%
0.6153846154 30
 
0.3%

NewState
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size622.4 KiB
TUNIS
4048 
ARIANA
3967 
BEN AROUS
1630 
MANOUBA
449 

Length

Max length9
Median length7
Mean length6.1278978
Min length5

Characters and Unicode

Total characters61855
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTUNIS
2nd rowTUNIS
3rd rowTUNIS
4th rowARIANA
5th rowARIANA

Common Values

ValueCountFrequency (%)
TUNIS 4048
40.1%
ARIANA 3967
39.3%
BEN AROUS 1630
16.1%
MANOUBA 449
 
4.4%

Length

2025-02-07T21:56:19.444669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T21:56:19.561244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
tunis 4048
34.5%
ariana 3967
33.8%
ben 1630
13.9%
arous 1630
13.9%
manouba 449
 
3.8%

Most occurring characters

ValueCountFrequency (%)
A 14429
23.3%
N 10094
16.3%
I 8015
13.0%
U 6127
9.9%
S 5678
 
9.2%
R 5597
 
9.0%
T 4048
 
6.5%
B 2079
 
3.4%
O 2079
 
3.4%
E 1630
 
2.6%
Other values (2) 2079
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 14429
23.3%
N 10094
16.3%
I 8015
13.0%
U 6127
9.9%
S 5678
 
9.2%
R 5597
 
9.0%
T 4048
 
6.5%
B 2079
 
3.4%
O 2079
 
3.4%
E 1630
 
2.6%
Other values (2) 2079
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 14429
23.3%
N 10094
16.3%
I 8015
13.0%
U 6127
9.9%
S 5678
 
9.2%
R 5597
 
9.0%
T 4048
 
6.5%
B 2079
 
3.4%
O 2079
 
3.4%
E 1630
 
2.6%
Other values (2) 2079
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 14429
23.3%
N 10094
16.3%
I 8015
13.0%
U 6127
9.9%
S 5678
 
9.2%
R 5597
 
9.0%
T 4048
 
6.5%
B 2079
 
3.4%
O 2079
 
3.4%
E 1630
 
2.6%
Other values (2) 2079
 
3.4%

balcony
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
True
5872 
False
4222 
ValueCountFrequency (%)
True 5872
58.2%
False 4222
41.8%
2025-02-07T21:56:19.661271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

heating
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
True
5291 
False
4803 
ValueCountFrequency (%)
True 5291
52.4%
False 4803
47.6%
2025-02-07T21:56:19.748271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

air_conditioning
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
False
5595 
True
4499 
ValueCountFrequency (%)
False 5595
55.4%
True 4499
44.6%
2025-02-07T21:56:19.835246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

garage
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
False
10094 
ValueCountFrequency (%)
False 10094
100.0%
2025-02-07T21:56:19.922244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

elevator
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
False
6653 
True
3441 
ValueCountFrequency (%)
False 6653
65.9%
True 3441
34.1%
2025-02-07T21:56:20.005524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

etage_count
Real number (ℝ)

Missing  Zeros 

Distinct8
Distinct (%)0.1%
Missing2007
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean0.64288364
Minimum0
Maximum12
Zeros3838
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-02-07T21:56:20.079515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72793182
Coefficient of variation (CV)1.1322917
Kurtosis10.176314
Mean0.64288364
Median Absolute Deviation (MAD)1
Skewness1.6473388
Sum5199
Variance0.52988473
MonotonicityNot monotonic
2025-02-07T21:56:20.175515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3838
38.0%
1 3459
34.3%
2 680
 
6.7%
3 81
 
0.8%
4 20
 
0.2%
6 5
 
< 0.1%
5 3
 
< 0.1%
12 1
 
< 0.1%
(Missing) 2007
19.9%
ValueCountFrequency (%)
0 3838
38.0%
1 3459
34.3%
2 680
 
6.7%
3 81
 
0.8%
4 20
 
0.2%
5 3
 
< 0.1%
6 5
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
6 5
 
< 0.1%
5 3
 
< 0.1%
4 20
 
0.2%
3 81
 
0.8%
2 680
 
6.7%
1 3459
34.3%
0 3838
38.0%

rez_de_chaussee_count
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct7
Distinct (%)0.1%
Missing2007
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean0.21392358
Minimum0
Maximum6
Zeros6688
Zeros (%)66.3%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-02-07T21:56:20.272525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52301853
Coefficient of variation (CV)2.4448849
Kurtosis13.40236
Mean0.21392358
Median Absolute Deviation (MAD)0
Skewness3.0998924
Sum1730
Variance0.27354838
MonotonicityNot monotonic
2025-02-07T21:56:20.357496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 6688
66.3%
1 1133
 
11.2%
2 224
 
2.2%
3 25
 
0.2%
4 12
 
0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
(Missing) 2007
 
19.9%
ValueCountFrequency (%)
0 6688
66.3%
1 1133
 
11.2%
2 224
 
2.2%
3 25
 
0.2%
4 12
 
0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 4
 
< 0.1%
4 12
 
0.1%
3 25
 
0.2%
2 224
 
2.2%
1 1133
 
11.2%
0 6688
66.3%

floor
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct16
Distinct (%)0.3%
Missing4217
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean2.1873405
Minimum-1
Maximum20
Zeros1164
Zeros (%)11.5%
Negative1
Negative (%)< 0.1%
Memory size79.0 KiB
2025-02-07T21:56:20.458523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum20
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.925817
Coefficient of variation (CV)0.88043767
Kurtosis4.2614921
Mean2.1873405
Median Absolute Deviation (MAD)1
Skewness1.3138118
Sum12855
Variance3.708771
MonotonicityNot monotonic
2025-02-07T21:56:20.550525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 1414
 
14.0%
0 1164
 
11.5%
2 1157
 
11.5%
3 817
 
8.1%
4 648
 
6.4%
5 277
 
2.7%
6 223
 
2.2%
7 124
 
1.2%
8 24
 
0.2%
9 15
 
0.1%
Other values (6) 14
 
0.1%
(Missing) 4217
41.8%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 1164
11.5%
1 1414
14.0%
2 1157
11.5%
3 817
8.1%
4 648
6.4%
5 277
 
2.7%
6 223
 
2.2%
7 124
 
1.2%
8 24
 
0.2%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 1
 
< 0.1%
11 1
 
< 0.1%
10 8
 
0.1%
9 15
 
0.1%
8 24
 
0.2%
7 124
1.2%
6 223
2.2%
5 277
2.7%
Distinct10078
Distinct (%)100.0%
Missing16
Missing (%)0.2%
Memory size12.7 MiB
2025-02-07T21:56:20.844843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5229
Median length1149
Mean length497.17136
Min length9

Characters and Unicode

Total characters5010493
Distinct characters588
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10078 ?
Unique (%)100.0%

Sample

1st rowGreen Immobilière vous propose à la vente un bel appartement dans une résidence sécurisée et proche de toutes commodités a el nasr 2. Il est composé : -un salon salle à manger avec terrasse -une salle d'eau -une cuisine équipée avec séchoir -deux chambres à coucher avec une salle de bain commune -une suite parentale -une place de parking au sous-sol Prix demandé : 400.000 MDT
2nd rowopportunité à saisir : appartement s+4 à vendre à L'Ariana, au sein d'une résidence privée avec stationnement collectif. l'appartement se compose d'un séjour lumineux avec balcon, de quatre chambres, ainsi que d'une cuisine et d'une salle de bain. titre foncier individuel. prix négociable.
3rd rowL'agence immobilière Good Life vous propose à la vente un appartement S+3, qui occupe le premier étage d’un immeuble R+5 avec double ascenseur, situé à Jardins d’El Menzah. Cet appartement, construit en 2006, offre une superficie totale de 157 (140 net). Il se compose d’un salon lumineux avec un grand balcon, de trois chambres à coucher équipées de dressings, d’une salle de bain, d’une salle d’eau, d’un dressing supplémentaire dans le couloir, ainsi que d’une cuisine avec séchoir. Une place de parking est incluse au sous-sol. L’appartement est équipé de chauffage central, de deux climatiseurs et d’un interphone. La résidence est sécurisée et bénéficie d’un service de gardiennage. Papier: TF en cours. Le prix demandé est de 365 000 dinars. Pour plus d’informations, veuillez contacter votre courtier en immobilier, Mr Slim Dhouib.
4th rowLa Croisette Immobilière Tunisie vous propose à la vente un appartement S+1 occupant le rez-de-chaussée d’une résidence gardée à La Nouvelle Soukra, à Ain Zaghouan. Ce dernier fait 77 mètres carrés et se compose d'un grand salon, d'une cuisine aménagée et équipée, d'une chambre à coucher avec placard attenante à une terrasse et d’une salle d'eau avec douche. L'appartement est équipé du chauffage central et de climatiseurs. Réf :MAV1783
5th rowA vendre un appartement de 83 net au titre foncier à l ariana essoughra tout prés de l université ESPRIT Ariana au 2ème étage dans une résidence prés de toutes commodités composé d un salon cuisine bien équipée deux chambres et sdb prix négociable.
ValueCountFrequency (%)
de 35363
 
4.3%
à 25279
 
3.1%
22137
 
2.7%
et 21785
 
2.7%
une 20702
 
2.5%
un 18768
 
2.3%
avec 16306
 
2.0%
appartement 12078
 
1.5%
salle 12021
 
1.5%
la 11672
 
1.4%
Other values (24606) 624900
76.1%
2025-02-07T21:56:21.303248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
811140
16.2%
e 549568
 
11.0%
a 315628
 
6.3%
n 293441
 
5.9%
s 258561
 
5.2%
i 249635
 
5.0%
t 240004
 
4.8%
r 239260
 
4.8%
u 219101
 
4.4%
o 175456
 
3.5%
Other values (578) 1658699
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5010493
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
811140
16.2%
e 549568
 
11.0%
a 315628
 
6.3%
n 293441
 
5.9%
s 258561
 
5.2%
i 249635
 
5.0%
t 240004
 
4.8%
r 239260
 
4.8%
u 219101
 
4.4%
o 175456
 
3.5%
Other values (578) 1658699
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5010493
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
811140
16.2%
e 549568
 
11.0%
a 315628
 
6.3%
n 293441
 
5.9%
s 258561
 
5.2%
i 249635
 
5.0%
t 240004
 
4.8%
r 239260
 
4.8%
u 219101
 
4.4%
o 175456
 
3.5%
Other values (578) 1658699
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5010493
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
811140
16.2%
e 549568
 
11.0%
a 315628
 
6.3%
n 293441
 
5.9%
s 258561
 
5.2%
i 249635
 
5.0%
t 240004
 
4.8%
r 239260
 
4.8%
u 219101
 
4.4%
o 175456
 
3.5%
Other values (578) 1658699
33.1%

date
Unsupported

Missing  Rejected  Unsupported 

Missing6908
Missing (%)68.4%
Memory size473.2 KiB

floor_description
Text

Missing 

Distinct3803
Distinct (%)75.7%
Missing5073
Missing (%)50.3%
Memory size999.5 KiB
2025-02-07T21:56:21.683495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6479
Median length943
Mean length74.626768
Min length14

Characters and Unicode

Total characters374701
Distinct characters114
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3132 ?
Unique (%)62.4%

Sample

1st rowa vendre etage de villa situe
2nd rows+3 occupant le rez-de-chaussee d'une residence gardee
3rd rowà coucher qui partage une salle de
4th rowun etage de villa situe
5th rowluxe au dernier etage avec une vue
ValueCountFrequency (%)
etage 5314
 
8.1%
au 5227
 
8.0%
residence 1693
 
2.6%
avec 1688
 
2.6%
une 1660
 
2.5%
1585
 
2.4%
situe 1438
 
2.2%
de 1432
 
2.2%
rdc 1410
 
2.2%
dans 1098
 
1.7%
Other values (2393) 42831
65.5%
2025-02-07T21:56:22.131376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 65699
17.5%
60355
16.1%
a 30752
 
8.2%
s 21058
 
5.6%
u 20664
 
5.5%
t 19583
 
5.2%
r 18200
 
4.9%
n 18089
 
4.8%
i 13763
 
3.7%
d 13452
 
3.6%
Other values (104) 93086
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 374701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 65699
17.5%
60355
16.1%
a 30752
 
8.2%
s 21058
 
5.6%
u 20664
 
5.5%
t 19583
 
5.2%
r 18200
 
4.9%
n 18089
 
4.8%
i 13763
 
3.7%
d 13452
 
3.6%
Other values (104) 93086
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 374701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 65699
17.5%
60355
16.1%
a 30752
 
8.2%
s 21058
 
5.6%
u 20664
 
5.5%
t 19583
 
5.2%
r 18200
 
4.9%
n 18089
 
4.8%
i 13763
 
3.7%
d 13452
 
3.6%
Other values (104) 93086
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 374701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 65699
17.5%
60355
16.1%
a 30752
 
8.2%
s 21058
 
5.6%
u 20664
 
5.5%
t 19583
 
5.2%
r 18200
 
4.9%
n 18089
 
4.8%
i 13763
 
3.7%
d 13452
 
3.6%
Other values (104) 93086
24.8%

Etat
Categorical

High correlation  Missing 

Distinct9
Distinct (%)0.7%
Missing8881
Missing (%)88.0%
Memory size583.9 KiB
nouveau
376 
bon état
361 
Bon état
275 
Nouveau
183 
à rénover
 
12
Other values (4)
 
6

Length

Max length9
Median length8
Mean length7.5391591
Min length4

Characters and Unicode

Total characters9145
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowNouveau
2nd rownouveau
3rd rownouveau
4th rownouveau
5th rowbon état

Common Values

ValueCountFrequency (%)
nouveau 376
 
3.7%
bon état 361
 
3.6%
Bon état 275
 
2.7%
Nouveau 183
 
1.8%
à rénover 12
 
0.1%
À rénover 2
 
< 0.1%
vide 2
 
< 0.1%
neuf é 1
 
< 0.1%
Neuf 1
 
< 0.1%
(Missing) 8881
88.0%

Length

2025-02-07T21:56:22.260375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T21:56:22.386375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bon 636
34.1%
état 636
34.1%
nouveau 559
30.0%
à 14
 
0.8%
rénover 14
 
0.8%
vide 2
 
0.1%
neuf 2
 
0.1%
é 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t 1272
13.9%
o 1209
13.2%
a 1195
13.1%
u 1120
12.2%
n 1027
11.2%
651
7.1%
é 651
7.1%
e 577
6.3%
v 575
6.3%
b 361
 
3.9%
Other values (8) 507
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1272
13.9%
o 1209
13.2%
a 1195
13.1%
u 1120
12.2%
n 1027
11.2%
651
7.1%
é 651
7.1%
e 577
6.3%
v 575
6.3%
b 361
 
3.9%
Other values (8) 507
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1272
13.9%
o 1209
13.2%
a 1195
13.1%
u 1120
12.2%
n 1027
11.2%
651
7.1%
é 651
7.1%
e 577
6.3%
v 575
6.3%
b 361
 
3.9%
Other values (8) 507
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1272
13.9%
o 1209
13.2%
a 1195
13.1%
u 1120
12.2%
n 1027
11.2%
651
7.1%
é 651
7.1%
e 577
6.3%
v 575
6.3%
b 361
 
3.9%
Other values (8) 507
 
5.5%

age
Categorical

High correlation  Missing 

Distinct9
Distinct (%)0.7%
Missing8833
Missing (%)87.5%
Memory size567.7 KiB
10-20 years
304 
Less than a year
285 
5-10 years
276 
1-5 years
215 
20-30 years
109 
Other values (4)
72 

Length

Max length19
Median length16
Mean length11.598731
Min length9

Characters and Unicode

Total characters14626
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLess than a year
2nd rowLess than a year
3rd rowLess than a year
4th row10-20 years
5th rowLess than a year

Common Values

ValueCountFrequency (%)
10-20 years 304
 
3.0%
Less than a year 285
 
2.8%
5-10 years 276
 
2.7%
1-5 years 215
 
2.1%
20-30 years 109
 
1.1%
30-50 years 56
 
0.6%
50-70 years 8
 
0.1%
70-100 years 4
 
< 0.1%
More than 100 years 4
 
< 0.1%
(Missing) 8833
87.5%

Length

2025-02-07T21:56:22.621346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T21:56:22.745366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
years 976
31.5%
10-20 304
 
9.8%
than 289
 
9.3%
less 285
 
9.2%
a 285
 
9.2%
year 285
 
9.2%
5-10 276
 
8.9%
1-5 215
 
6.9%
20-30 109
 
3.5%
30-50 56
 
1.8%
Other values (4) 20
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1839
12.6%
a 1835
12.5%
e 1550
10.6%
s 1546
10.6%
r 1265
8.6%
y 1261
8.6%
0 1250
8.5%
- 972
6.6%
1 803
5.5%
5 555
 
3.8%
Other values (9) 1750
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1839
12.6%
a 1835
12.5%
e 1550
10.6%
s 1546
10.6%
r 1265
8.6%
y 1261
8.6%
0 1250
8.5%
- 972
6.6%
1 803
5.5%
5 555
 
3.8%
Other values (9) 1750
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1839
12.6%
a 1835
12.5%
e 1550
10.6%
s 1546
10.6%
r 1265
8.6%
y 1261
8.6%
0 1250
8.5%
- 972
6.6%
1 803
5.5%
5 555
 
3.8%
Other values (9) 1750
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1839
12.6%
a 1835
12.5%
e 1550
10.6%
s 1546
10.6%
r 1265
8.6%
y 1261
8.6%
0 1250
8.5%
- 972
6.6%
1 803
5.5%
5 555
 
3.8%
Other values (9) 1750
12.0%

delegation
Categorical

High correlation  Missing 

Distinct45
Distinct (%)1.5%
Missing7141
Missing (%)70.7%
Memory size585.8 KiB
La Marsa
419 
Ariana Ville
358 
La Soukra
355 
Ain Zaghouan
299 
El Mourouj
184 
Other values (40)
1338 

Length

Max length21
Median length17
Mean length9.7389096
Min length5

Characters and Unicode

Total characters28759
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAriana Ville
2nd rowEl Menzah
3rd rowAriana Ville
4th rowAin Zaghouan
5th rowRaoued

Common Values

ValueCountFrequency (%)
La Marsa 419
 
4.2%
Ariana Ville 358
 
3.5%
La Soukra 355
 
3.5%
Ain Zaghouan 299
 
3.0%
El Mourouj 184
 
1.8%
El Menzah 143
 
1.4%
Bab Bhar 126
 
1.2%
Raoued 94
 
0.9%
Rades 75
 
0.7%
Nouvelle Medina 70
 
0.7%
Other values (35) 830
 
8.2%
(Missing) 7141
70.7%

Length

2025-02-07T21:56:22.883361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
la 829
14.8%
el 586
 
10.4%
marsa 419
 
7.5%
ville 358
 
6.4%
ariana 358
 
6.4%
soukra 355
 
6.3%
ain 299
 
5.3%
zaghouan 299
 
5.3%
mourouj 184
 
3.3%
bab 146
 
2.6%
Other values (50) 1785
31.8%

Most occurring characters

ValueCountFrequency (%)
a 5212
18.1%
2665
 
9.3%
r 1999
 
7.0%
l 1556
 
5.4%
o 1537
 
5.3%
u 1533
 
5.3%
n 1497
 
5.2%
i 1460
 
5.1%
e 1334
 
4.6%
M 1020
 
3.5%
Other values (34) 8946
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5212
18.1%
2665
 
9.3%
r 1999
 
7.0%
l 1556
 
5.4%
o 1537
 
5.3%
u 1533
 
5.3%
n 1497
 
5.2%
i 1460
 
5.1%
e 1334
 
4.6%
M 1020
 
3.5%
Other values (34) 8946
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5212
18.1%
2665
 
9.3%
r 1999
 
7.0%
l 1556
 
5.4%
o 1537
 
5.3%
u 1533
 
5.3%
n 1497
 
5.2%
i 1460
 
5.1%
e 1334
 
4.6%
M 1020
 
3.5%
Other values (34) 8946
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5212
18.1%
2665
 
9.3%
r 1999
 
7.0%
l 1556
 
5.4%
o 1537
 
5.3%
u 1533
 
5.3%
n 1497
 
5.2%
i 1460
 
5.1%
e 1334
 
4.6%
M 1020
 
3.5%
Other values (34) 8946
31.1%

municipality
Text

Missing 

Distinct192
Distinct (%)6.0%
Missing6898
Missing (%)68.3%
Memory size429.9 KiB
2025-02-07T21:56:23.179375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length29
Median length22
Mean length11.441802
Min length5

Characters and Unicode

Total characters36568
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.4%

Sample

1st rowCite Ennasr 2
2nd rowEl Menzah 9
3rd rowCite Ennasr 2
4th rowAin Zaghouan
5th rowAriana Essoughra
ValueCountFrequency (%)
el 796
 
11.3%
cite 459
 
6.5%
la 366
 
5.2%
aouina 249
 
3.5%
2 222
 
3.2%
ain 211
 
3.0%
zaghouan 211
 
3.0%
ennasr 208
 
3.0%
1 204
 
2.9%
jardins 198
 
2.8%
Other values (197) 3917
55.6%
2025-02-07T21:56:23.606721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4790
 
13.1%
3845
 
10.5%
n 2498
 
6.8%
e 2430
 
6.6%
r 2353
 
6.4%
i 1961
 
5.4%
o 1898
 
5.2%
u 1817
 
5.0%
l 1399
 
3.8%
E 1160
 
3.2%
Other values (52) 12417
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4790
 
13.1%
3845
 
10.5%
n 2498
 
6.8%
e 2430
 
6.6%
r 2353
 
6.4%
i 1961
 
5.4%
o 1898
 
5.2%
u 1817
 
5.0%
l 1399
 
3.8%
E 1160
 
3.2%
Other values (52) 12417
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4790
 
13.1%
3845
 
10.5%
n 2498
 
6.8%
e 2430
 
6.6%
r 2353
 
6.4%
i 1961
 
5.4%
o 1898
 
5.2%
u 1817
 
5.0%
l 1399
 
3.8%
E 1160
 
3.2%
Other values (52) 12417
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4790
 
13.1%
3845
 
10.5%
n 2498
 
6.8%
e 2430
 
6.6%
r 2353
 
6.4%
i 1961
 
5.4%
o 1898
 
5.2%
u 1817
 
5.0%
l 1399
 
3.8%
E 1160
 
3.2%
Other values (52) 12417
34.0%

Interactions

2025-02-07T21:56:13.138245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:00.641101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:01.902500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:03.056479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:04.365260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:05.764254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:06.992370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:08.360006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:09.590078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:10.741131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:11.885210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:13.237236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:00.779419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:02.013493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:03.162208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:04.510252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:05.878296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:07.102438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:08.467057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:09.700094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:10.845129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:12.092211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:13.330244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:00.875407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:02.102487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:03.266205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:04.744254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:05.978283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:07.219409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:08.576046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:09.798095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:10.942128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:12.191211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:13.441267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:01.050407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:02.214480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:03.376208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:04.852261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:06.100274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:07.342439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:08.683545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:09.909117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:11.048129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:12.302212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:13.546243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:01.154408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:02.321489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:03.483210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:04.954262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:06.209266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:07.444438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:08.796538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:10.013108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:11.157159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:12.407212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:13.644243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:01.272407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:02.430492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:03.634233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:05.076262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:06.330275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:07.572452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:08.909548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:10.129117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:11.269178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:12.519243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:13.742773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:01.374943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:02.537489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:03.793206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:05.221253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:06.437773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:07.673433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:09.011547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:10.229615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:11.372159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:12.622243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:13.842784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:01.487481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:02.638491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:03.903208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:05.332324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:06.551394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:07.815957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:09.121547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:10.332615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:11.476150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:12.727238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:13.939760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:01.591490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:02.753498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:04.014260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:05.434349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:06.660370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:07.923959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:09.260539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:10.431614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:11.579205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:12.830243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:14.038760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:01.694489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:02.857489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:04.120260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:05.548231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:06.779372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:08.043961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:09.381545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:10.538615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:11.682210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:12.934245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:14.142755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:01.806511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:02.958481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:04.243255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:05.657230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:06.896381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:08.262017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:09.487546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:10.644131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:11.786203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T21:56:13.037242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2025-02-07T21:56:23.740693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EtatNewDelegationNewDelegationReportNewStateNewStateReportageair_conditioningbalconybathroomsbedroomsdelegationelevatorequipped_kitchenetage_countfloorheatingnewMunipReportparkingpricerez_de_chaussee_countroomsstatesurface
Etat1.0000.3320.0000.4350.0000.3540.3200.2191.0001.0000.0000.2180.3010.0000.0670.3270.1440.1880.0000.0000.0000.5961.000
NewDelegation0.3321.0000.3080.9290.0570.2770.3210.1590.0000.1040.8890.2320.1880.0350.1000.2940.2650.2040.0300.0540.0340.8510.049
NewDelegationReport0.0000.3081.0000.1250.2360.0750.0490.076-0.058-0.0610.4800.0540.0650.068-0.0530.0230.6240.0540.0300.016-0.0650.1100.004
NewState0.4350.9290.1251.0000.0400.2240.1920.0910.0060.0090.8670.1240.1340.0220.0750.1780.2410.0830.0050.0080.0640.8610.000
NewStateReport0.0000.0570.2360.0401.0000.1490.0340.0000.003-0.0301.0000.0220.0000.011-0.0030.0320.1620.041-0.031-0.010-0.0291.000-0.029
age0.3540.2770.0750.2240.1491.0000.2650.0991.0001.0000.0000.3710.3800.1570.0720.3410.0610.2590.0590.0540.0000.2281.000
air_conditioning0.3200.3210.0490.1920.0340.2651.0000.3050.0000.0110.3120.3390.4120.0740.1010.6950.1190.3340.0000.0470.0450.2150.010
balcony0.2190.1590.0760.0910.0000.0990.3051.0000.0000.0040.1350.2170.2490.0920.0950.2810.0460.2190.0140.0270.0660.0940.011
bathrooms1.0000.000-0.0580.0060.0031.0000.0000.0001.0000.3061.0000.0070.000-0.0160.0650.005-0.0150.0000.315-0.0140.2420.0000.321
bedrooms1.0000.104-0.0610.009-0.0301.0000.0110.0040.3061.0000.1450.0030.0000.1230.0900.015-0.0350.0050.372-0.0120.6870.0240.585
delegation0.0000.8890.4800.8671.0000.0000.3120.1351.0000.1451.0000.2081.0000.0900.1710.2820.3970.3271.0000.0590.2100.9931.000
elevator0.2180.2320.0540.1240.0220.3710.3390.2170.0070.0030.2081.0000.4280.0000.2660.3880.0480.3020.0160.1210.0590.1210.007
equipped_kitchen0.3010.1880.0650.1340.0000.3800.4120.2490.0000.0001.0000.4281.0000.0370.0880.3350.0640.2300.0000.0000.1850.1570.015
etage_count0.0000.0350.0680.0220.0110.1570.0740.092-0.0160.1230.0900.0000.0371.0000.4800.0810.0590.0350.009-0.1610.0820.0280.047
floor0.0670.100-0.0530.075-0.0030.0720.1010.0950.0650.0900.1710.2660.0880.4801.0000.115-0.0300.055-0.060-0.7520.0580.0680.015
heating0.3270.2940.0230.1780.0320.3410.6950.2810.0050.0150.2820.3880.3350.0810.1151.0000.1020.3140.0100.0260.0340.1830.010
newMunipReport0.1440.2650.6240.2410.1620.0610.1190.046-0.015-0.0350.3970.0480.0640.059-0.0300.1021.0000.0320.1310.0020.0120.2750.034
parking0.1880.2040.0540.0830.0410.2590.3340.2190.0000.0050.3270.3020.2300.0350.0550.3140.0321.0000.0000.0090.0480.1050.066
price0.0000.0300.0300.005-0.0310.0590.0000.0140.3150.3721.0000.0160.0000.009-0.0600.0100.1310.0001.0000.0420.2861.0000.655
rez_de_chaussee_count0.0000.0540.0160.008-0.0100.0540.0470.027-0.014-0.0120.0590.1210.000-0.161-0.7520.0260.0020.0090.0421.000-0.0380.0260.010
rooms0.0000.034-0.0650.064-0.0290.0000.0450.0660.2420.6870.2100.0590.1850.0820.0580.0340.0120.0480.286-0.0381.0000.0510.499
state0.5960.8510.1100.8611.0000.2280.2150.0940.0000.0240.9930.1210.1570.0280.0680.1830.2750.1051.0000.0260.0511.0000.000
surface1.0000.0490.0040.000-0.0291.0000.0100.0110.3210.5851.0000.0070.0150.0470.0150.0100.0340.0660.6550.0100.4990.0001.000

Missing values

2025-02-07T21:56:14.382822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-07T21:56:14.795588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-07T21:56:15.250590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

pricelocationstatebedroomsroomsbathroomssurfaceparkingequipped_kitchennewMunipReportnewMunipNewDelegationReportNewDelegationNewStateReportNewStatebalconyheatingair_conditioninggarageelevatoretage_countrez_de_chaussee_countfloordescriptiondatefloor_descriptionEtatagedelegationmunicipality
0680000Jardins de CarthageTunis3.04.01.0150.0YesYes1.00JARDINS DE CARTHAGE1.000000MARSA1.0TUNISnonononoNo0.00.0NaNNaNNaNNaNNaNNaNNaNNaN
1560000Les Jardins du lac 2Tunis2.03.01.0125.0YesYes1.00LES JARDINS1.000000BAB BHAR1.0TUNISYesnoYesnoYesNaNNaN7.0NaNNaNNaNNaNNaNNaNNaN
2480000Les berges du lac 1Tunis3.05.02.0109.0Yesno0.96BERGE DU LAC0.545455RADES1.0TUNISnonononoNo0.00.0NaNNaNNaNNaNNaNNaNNaNNaN
3410000Ennasr 1-2Ariana3.04.02.0130.0Yesno1.00ENNASR1.000000EL MNIHLA1.0ARIANAnonononoNo0.00.0NaNNaNNaNNaNNaNNaNNaNNaN
4399000AouinaAriana2.03.02.0115.0nono1.00AOUINA1.000000SOUKRA1.0ARIANAnonononoNo0.00.0NaNNaNNaNNaNNaNNaNNaNNaN
5395000La SoukraAriana3.05.01.0114.0YesYes1.00SOUKRA1.000000SOUKRA1.0ARIANAYesnoYesnoYes0.00.0NaNNaNNaNNaNNaNNaNNaNNaN
6385000La SoukraAriana2.03.0NaN120.0nono1.00SOUKRA1.000000SOUKRA1.0ARIANAnonononoNo0.00.0NaNNaNNaNNaNNaNNaNNaNNaN
7385000La SoukraAriana2.03.02.091.0YesYes1.00SOUKRA1.000000SOUKRA1.0ARIANAYesnononoYesNaNNaN0.0NaNNaNNaNNaNNaNNaNNaN
8375000ChotranaAriana2.03.01.092.0nono1.00CHOTRANA1.000000SOUKRA1.0ARIANAnonononoNo0.00.0NaNNaNNaNNaNNaNNaNNaNNaN
9360000Ennasr 1-2Ariana2.04.02.090.0YesYes1.00ENNASR1.000000EL MNIHLA1.0ARIANAYesYesnonoYesNaNNaN7.0NaNNaNNaNNaNNaNNaNNaN
pricelocationstatebedroomsroomsbathroomssurfaceparkingequipped_kitchennewMunipReportnewMunipNewDelegationReportNewDelegationNewStateReportNewStatebalconyheatingair_conditioninggarageelevatoretage_countrez_de_chaussee_countfloordescriptiondatefloor_descriptionEtatagedelegationmunicipality
10084210000NaNNaN3.03.01.088.0Yesno0.928571CITE EL KHADRA0.75RADES0.571429TUNISYesnononoNoNaNNaN3.0<p>Cet appartement <strong>S+3</strong> situé au <strong>3ème étage</strong>, doté d'un <strong> point d'eau et une salle de bain</strong>, d'un <strong>balcon</strong> attenant au salon, <strong>trois chambres</strong> avec <strong>dressings</strong> et d'une <strong>cuisine spacieuse</strong>.</p><p>Niché dans un quartier résidentiel paisible, il offre un cadre de vie idéal à proximité des <strong>écoles</strong>, <strong>commerces</strong> et <strong>transports en commun</strong>.</p><p>Une <strong>place de parking</strong> est disponible.</p> 3 piècesNaNNaNNaN30-50 yearsNaNCité El Khadra
10085470000NaNNaN2.02.01.0103.0Yesno1.000000CENTRE URBAIN NORD1.00CITE EL KHADRA/CITÉ EL KHADRA1.000000TUNISnoYesYesnoYesNaNNaN1.0<p>Un <strong>appartement S+2</strong> situé au Centre Urbain Nord, d'une superficie de <strong>103 m²</strong>, au <strong>1er étage</strong> d'un immeuble. Ce bien se compose d'un <strong>salon spacieux</strong>, de <strong>deux chambres</strong> confortables et d'une <strong>salle de bain</strong> bien <strong>aménagée</strong>. <strong>La cuisine</strong> est entièrement <strong>équipée</strong> avec une <strong>plaque de cuisson</strong>, une <strong>hotte aspirante</strong>, un <strong>four</strong> et un <strong>sèche-linge.</strong> L'appartement bénéficie également de la <strong>climatisation centrale</strong> et du <strong>chauffage central</strong> pour un confort optimal tout au long de l'année. De plus, il dispose d'une <strong>place de parking</strong> dédiée. Il offre un cadre de vie agréable avec toutes les commodités nécessaires à proximité.</p> 2 piècesNaNNaNNaN5-10 yearsNaNCentre Urbain Nord
10086267000NaNNaN2.03.01.090.0Yesno1.000000BOU MHEL1.00BOU MHEL EL BASSATINE1.000000BEN AROUSYesYesnonoNoNaNNaN1.0<p>Appartement S+2 en <strong>direct promoteur</strong> situé dans une résidence en <strong>R+4 à Boumhel El Bassatine</strong>. Plusieurs typologies sont disponibles et les finitions sont en cours avec une <strong>remise des clés</strong> prévue pour <strong>juin 2025</strong>. Cet appartement comprend une entrée menant à un salon <strong>spacieux</strong> revêtu de marbre. La cuisine entièrement <strong>équipée</strong> et <strong>aménagée</strong> dispose également d'un <strong>séchoir</strong>. La partie nuit se compose de deux chambres l'une avec un <strong>dressing</strong> et l'autre donnant accès à une <strong>terrasse</strong>. Une salle de bain commune est à disposition. De plus, <strong>une place de parking</strong> est proposée au prix de 18 000TND.</p> 3 piècesNaNNaNNaNLess than a yearNaNBou Mhel El-Bassatine
10087490000NaNNaN1.03.01.0152.0Yesno1.000000ENNASR1.00EL MNIHLA1.000000ARIANAYesYesYesnoNoNaNNaN1.0<p>Ce duplex fait partie d’une <strong>résidence calme et sécurisée à Ennasr 2</strong>. Il bénéficie d'une<strong> entrée indépendante</strong> ainsi qu’un accès à <strong>une terrasse.</strong> Le premier niveau abrite <strong>une chambre à coucher équipée d’un dressing ainsi qu’une salle d’eau</strong>. <strong>Quelques marches nous mènent au rez-de-chaussée qui loge une pièce de vie accueillante,</strong> elle permet d’aménager <strong>un salon et une salle à manger</strong>. <strong>La cuisine</strong> est totalement indépendante, elle est équipée d<strong>’une plaque de cuisson et une hotte aspirante</strong>, elle est complétée par <strong>un séchoir fonctionnel.</strong> Le premier étage comprend <strong>deux chambres à coucher avec dressing</strong> et elles se partagent <strong>une salle de bain avec jacuzzi.</strong> Ce duplex est équipé <strong>d’un chauffage central et d'une climatisation en split système</strong>. <strong>Une place de parking</strong> est disponible pour les futurs occupants.</p> 3 piècesNaNNaNNaN10-20 yearsNaNEnnasr
10088180000NaNNaN2.02.01.093.0Yesno0.928571CITE EL KHADRA0.75RADES0.615385ARIANAYesnononoNo0.01.00.0<p> </p><p>Cet appartement se situe au rez-de-chaussée d'une résidence gardée et sécurisée, au plein cœur de la cité El Khadra.</p><p>Un salon lumineux est accessible depuis l'entrée grâce à ses ouvertures qui mènent à un balcon.</p><p>La cuisine est aménagée avec des éléments de rangement. Elle est annexée par un séchoir.</p><p>Le compartiment nuit comprend deux chambres à coucher, une salle de bain et un point d'eau pour les recevoir</p><p>emplacement de parking collectif</p> 2 piècesNaNse situe au rez-de-chaussee d'une residence gardeeNaN50-70 yearsNaNCité El Khadra
10089385000NaNNaN3.03.01.0139.0Yesno1.000000ENNASR1.00EL MNIHLA1.000000ARIANAYesYesYesnoYes1.00.0NaN<p>Cet appartement est situé au <strong>sixième</strong> étage d'une résidence <strong>sécurisée</strong>. Dès votre arrivée, un <strong>hall</strong> d'entrée <strong>accueillant</strong> équipée d’un dressing vous y attend. Le salon <strong>lumineux</strong> offre un espace <strong>agréable</strong>. La cuisine <strong>soigneusement</strong> équipée et <strong>aménagée</strong>, elle dispose d'une <strong>plaque</strong> de <strong>cuisson</strong> et d'un <strong>four électrique</strong>, ainsi que d'un espace dédié pour un <strong>séchoir</strong>. <strong>Une salle de douche</strong> complète la partie jour. La partie nuit de l'appartement comprend une <strong>suite parentale</strong> avec <strong>une salle de bain</strong>, ainsi que <strong>deux chambres à coucher dotée d'un dressing chacune</strong> et dont une complétée par <strong>un balcon</strong>. L’appartement est doté <strong>d’un chauffage central et d’une climatisation par split système. </strong><strong>Une place de parking</strong> est également disponible pour les futurs propriétaires.</p> 3 piècesNaNsitue au <strong>sixieme</strong> etage d'une residence <strong>securisee</strong>.NaN10-20 yearsNaNEnnasr
10090320000NaNNaN3.04.01.0125.0Yesno0.928571CITE EL KHADRA0.75RADES0.571429TUNISYesnoYesnoNoNaNNaN1.0<p>Cet appartement de type S+3, situé au <strong>premier étage</strong>, comprend <strong>trois chambres</strong> ainsi qu'un <strong>salon spacieux</strong> avec <strong>un balcon vast</strong>e et un point d'eau, une salle de bain, et une cuisine lumineuse.</p><p>Niché dans un quartier résidentiel calme , il offre un cadre de vie idéal à proximité des <strong>écoles</strong>, <strong>commerces</strong> et <strong>transports en commun</strong>.</p><p>Une <strong>place de parking</strong> collective.</p> 4 piècesNaNNaNNaN30-50 yearsNaNCité El Khadra
10091340000NaNNaN2.03.01.085.0Yesno1.000000GAMMARTH1.00MARSA1.000000TUNISnonononoYes0.00.0NaN<p>Cet appartement est situé au sein d'une résidence à Gammarth, s'étendant sur deux niveaux et équipée d'un ascenseur.</p><p>L'entrée mène à un hall spacieux qui donne accès au salon avec salle à manger.</p><p>La cuisine dispose d'un séchoir.</p><p>Une salle d'eau est disponible pour les invités dans cette partie jour.</p><p>La partie nuit comprend deux chambres à coucher, chacune dotée de dressings et d'une salle d'eau avec baignoire.</p><p>Une place de parking est également disponible pour les futurs propriétaires.</p> 3 piècesNaNNaNNaN10-20 yearsNaNGammarth
10092290000NaNNaN3.03.01.0127.0Yesno0.769231RESIDENCE TYPE0.75RADES1.000000ARIANAYesnononoNoNaNNaN1.0<p>Cet appartement S+3 <strong>direct promoteur</strong>, situé dans une résidence en R+2 à <strong>Borj Louzir</strong> . Plusieurs typologies sont disponibles et les finitions sont en cours avec une remise des clés prévue pour <strong>février 2025</strong>. Cet appartement comprend une entrée menant à un salon spacieux revêtu de <strong>marbre</strong> qui donne accès à un balcon. La cuisine entièrement <strong>équipée</strong> et <strong>aménagée</strong> dispose également d'un balcon. La partie nuit loge trois chambres à coucher, dont chacune équipée avec un <strong>dressing</strong>, ainsi qu'une salle de bain commune qui complète cet espace. De plus, une place de parking est proposée au prix de 15 000 TND.</p> 3 piècesNaNNaNNaNLess than a yearNaNAriana
10093135000NaNNaN1.02.01.057.0Yesno0.769231RESIDENCE TYPE0.75RADES1.000000ARIANAYesYesYesnoNoNaNNaN1.0C'est un appartement<strong> S+1</strong> en <strong>direct promoteur,</strong> situé dans une résidence en <strong>R+2.</strong> Plusieurs typologies sont disponibles et les finitions sont en cours avec une remise des clés prévue pour <strong>février 2025</strong>. Cet appartement comprend une entrée menant à un salon <strong>spacieux</strong> revêtu de marbre qui donne accès à un balcon. La cuisine entièrement équipée et aménagée dispose également d'un <strong>balcon</strong>. La partie nuit se compose d'une chambre avec un <strong>dressing</strong>, ainsi qu'une salle de bain commune qui complète cet espace. De plus, une<strong> place de parking</strong> est proposée au prix de 15 000 TND. 2 piècesNaNNaNNaNLess than a yearNaNAriana